Task Allocation Strategy for MEC-Enabled IIoTs via Bayesian Network Based Evolutionary Computation

Abstract

The industrial Internet of Things (IIoTs) are well deployed to monitor pollutant emissions or device statuses in the industrial factory, especially in the chemical plants. To give a quick response of monitoring results, the industrial big data generated by IIoTs containing various tasks need to be processed as soon as possible. However, the priority constraints among the tasks generated by sensors are not considered in the existing architectures, which may result in the delayed response. Thus, in this article, we propose a mobile edge computing (MEC)-enabled architecture considering the priority constraints among tasks with the objective to minimize the response time. The tasks can be executed in MEC servers or cloud servers based on task complexity. Traditional methods search the optimal task allocation strategy through a set of initial strategies and a optimizer without the consideration of relationship among tasks. Thus, we propose a Bayesian network based evolutionary algorithm (BNEA) for optimizing a task allocation strategy. To fully consider the priority among tasks, the BNEA studies a Bayesian network based decomposition strategy in which the tasks are decomposed based on the relationship reflected by the learned Bayesian network structure. The BNEA searches the optimal task allocation strategy with the help of decomposed tasks cooperatively. Moreover, we propose a probability-based update strategy for particles to avoid draping into local optima. The experimental results verify that the BNEA can achieve the best response time through the corresponding task allocation strategy, which means that the data generated from the IIoTs can be transmitted to the destination in the shortest time.